Wearable AI for Cardiovascular Health Monitoring: Enabling Early Detection and Prevention

Abstract

Despite advancements in medicine, cardiovascular diseases (CVD) remain the leading cause of death in the world, highlighting the urgent need for continuous tracking and early detection. Wearable technology powered by artificial intelligence (AI) enables real-time, non-invasive monitoring of cardiovascular health. This study investigates the potential of wearable technology and artificial intelligence (AI)-based predictive analytics to revolutionize the early diagnosis and prevention of coronary vascular disease (CVD). Machine learning (ML) algorithms include decision trees, random forests, support vector machines, and deep neural networks that analyze medical data such as heart rate variability, activity levels, and sleep maintenance to detect subtle cardiovascular risk factors. These models can detect deviations earlier than conventional diagnostics, and an individualized data-driven therapy can also be designed for them. Wearable AI systems paired with imaging, genetics, and electronic health record (EHR) data provide a holistic view of patient health. However, challenges like data privacy, algorithmic bias, and clinical integration must be addressed to ensure responsible adoption. This study aims to maximize the potential of wearable AI to enable proactive health management by reviewing the current status of wearable AI, featuring recent advances, examples of use cases, and implementation methods in cardiovascular care.